• 한국정밀공학회지
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• 제15권1호
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• pp.137-144
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• 1998

For a model system consisted of four pneumatic cylinders with strokes of 10, 20, 40 and 80 mm, investigation was carried out experimentally and numerically about the reliability of system with elastic and viscous load. The elastic load affects the performance of each cylinder in cylinder series, and changes the time lag and the velocity of the piston which makes the positioning control rather difficult. Taking the effects of the elastic load into consideration, positioning can be carried out comparatively smoothly by only adjusting the driving timing. The effect of a viscous load reduces the vibration of each moving body in the cylinder series and also reduces the over-travelled distance which happens when several cylinders move at the same time. For reasons, a positioning with a viscous load can be relatively smoothly carried out even without the timing control.

• 대한영양사협회학술지
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• 제9권2호
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• pp.149-158
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• 2003

This study investigated changes in the household expenditure for food outside home using the time-series family expenditure data during 1982-2002. Major findings were as following: first, expenditure for food outside home had been increasing, while over all level of the food expenditure had been decreasing; second, two thirds of the total amount of expenditure for food outside home were for regular meals; the proportion of food outside home which were paid to alcohols and other beverages have been decreasing since 1999; lastly, over the half of the total expenditure for food outside home had been spent on Korean food. Based on the results implications for consumer trends for food and food industry were provided.

• Journal of the Korean Data and Information Science Society
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• 제28권4호
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• pp.889-898
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• 2017

In this research multiple change-points estimation for South Korean electricity generation data is considered. We analyze the South Korean electricity data via deterministically trending dynamic time series model with multiple structural changes in trends in a Bayesian approach. The number of change-points and the timing are unknown. The goal is to find the best model with the appropriate number of change-points and the length of the segments. A genetic algorithm is implemented to solve this optimization problem with a variable dimension of parameters. We estimate the structural change-points for South Korean electricity generation data and Nile River flow data additionally.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권11호
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• pp.5476-5496
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• 2016

Energy efficiency of resource-constrained wireless sensor networks is critical in applications such as real-time monitoring/surveillance. To improve the energy efficiency and reduce the energy consumption, the time series data can be compressed before transmission. However, most of the compression algorithms for time series data were developed only for single variate scenarios, while in practice there are often multiple sensor nodes in one application and the collected data is actually multivariate time series. In this paper, we propose to compress the time series data by the Lasso (least absolute shrinkage and selection operator) approximation. We show that, our approach can be naturally extended for compressing the multivariate time series data. Our extension is novel since it constructs an optimal projection of the original multivariates where the best energy efficiency can be realized. The two algorithms are named by ULasso (Univariate Lasso) and MLasso (Multivariate Lasso), for which we also provide practical guidance for parameter selection. Finally, empirically evaluation is implemented with several publicly available real-world data sets from different application domains. We quantify the algorithm performance by measuring the approximation error, compression ratio, and computation complexity. The results show that ULasso and MLasso are superior to or at least equivalent to compression performance of LTC and PLAMlis. Particularly, MLasso can significantly reduce the smooth multivariate time series data, without breaking the major trends and important changes of the sensor network system.

• 한국컴퓨터정보학회논문지
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• 제25권2호
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• pp.31-39
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• 2020

본 논문에서는 시계열 데이터를 기반으로 합성 벤치마크를 생성하는 기법을 소개한다. IoT 기기에서 측정되는 많은 데이터는 시간에 따른 수치 변화를 측정하는 시계열적 특성이 있다. 하지만 긴 기간 동안 측정되는 데이터를 일반화된 시계열 데이터로 모델링하기 힘든 문제점이 존재한다. 이런 문제를 개선하기 위해 본 논문에서는 BST-IGT 모델을 소개한다. BST-IGT 모델은 전체 데이터를 시계열 모델링이 쉬운 구간으로 분리하여 생성 데이터를 템플릿으로 수집하고 이를 기반으로 특성을 공유하거나 변형되는 새로운 합성 벤치마크를 생성한다. 제안된 모델링 기법을 이용하여 신규 벤치마크를 생성한 결과, 기존 데이터의 통계적 특성을 유지하는 합성 벤치마크와 다른 벤치마크와의 혼합으로 여러 특성을 가지는 벤치마크의 생성을 수행할 수 있었다.

• 대한원격탐사학회지
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• 제27권6호
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• pp.637-651
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• 2011

This study presents a rule-based urban image classification method for time series analysis of changes in the vicinity of Asan-si and Cheonan-si in Chungcheongnam-do, using Landsat satellite images (1991-2006). The area has been highly developed through the relocation of industrial facilities, land development, construction of a high-speed railroad, and an extension of the subway. To determine the yearly changing pattern of the urban area, eleven classes were made depending on the trend of development. An algorithm was generalized for the rules to be applied as an unsupervised classification, without the need of training area. The analysis results show that the urban zone of the research area has increased by about 1.53 times, and each correlation graph confirmed the distribution of the Built Up Index (BUI) values for each class. To evaluate the rule-based classification, coverage and accuracy were assessed. When Optimal allowable factor=0.36, the coverage of the rule was 98.4%, and for the test using ground data from 1991 to 2006, overall accuracy was 99.49%. It was confirmed that the method suggested to determine the maximum allowable factor correlates to the accuracy test results using ground data. Among the multiple images, available data was used as best as possible and classification accuracy could be improved since optimal classification to suit objectives was possible. The rule-based urban image classification method is expected to be applied to time series image analyses such as thematic mapping for urban development, urban development, and monitoring of environmental changes.

• 한국컴퓨터정보학회논문지
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• 제16권2호
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• pp.225-233
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• 2011

주식 가격이나 경제 지표, 사회적 현상의 추세나 변화 등은 통상 시간에 따라 변화하기 때문에 시계열 자료로 구분된다. 시계열 자료는 시간 축에 대해 변화하는 자료의 표현 가치뿐 아니라 그 변화 추세나 향후 방향성까지 제시할 수 있다는 점에서 이에 대한 방법론에 대해 많은 연구와 노력이 지속되어 왔다. 본 논문에서는 전통적으로 예측 모형을 구축하여 예측하는 방법을 취하되 그 모형이 복잡하고 정교한 모델을 활용하여 예측 정확도를 높이려는 시도와는 달리 자료 클러스터링 방법과 자료 구간 선정을 통해 예측정확도를 높이려 시도하였다. 기본 모델은 마코프 모델이다. 구간별 유사 구간을 추출하여 모델링하는 구간별 모델링 방법과 클러스터링을 통한 그룹별 모델링을 통해 모델의 예측정확도를 개선하려 시도하였다. 실험을 통해 클러스터링을 거친 그룹별 마코프 모델이 정확도를 개선 시켰으나 예측율은 현저히 떨어지는 결과를 낳았다.

• 한국컴퓨터정보학회논문지
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• 제28권9호
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• pp.159-165
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• 2023

본 연구는 공공기관 경영 효율성의 시계열적 변화 양상을 살펴보고, 이를 통해 재무적 성과와의 관련성을 검증해보고자 한다. 구체적으로 정부의 경영평가 결과에 따라 상위 그룹과 하위 그룹으로 구분하고, 평가시점 이전 그룹별 경영 효율성이 어떻게 변화하였는지를 살펴보았다. 경영효율성은 공공기관 경영정보 공개시스템의 자료를 이용하여 자료포락분석을 통해 측정하였다. 본 연구의 결과는 다음과 같다. 첫째, 경영평가 결과 상위 그룹에 속한 기관의 경영 효율성은 증가하는 방향으로 변화하였으나, 하위 그룹에 속한 기관의 경영 효율성은 감소하는 방향으로 변화하였다. 둘째, 경영평가 결과 상위 그룹에 속한 기관은 하위 기관에 속한 기관에 비해 상대적으로 높은 경영 효율성을 나타내었다. 이러한 결과는 공기업의 경영 효율성이 경영평가 결과, 즉 재무적 성과와 관련이 있음을 의미한다. 본 연구의 결과는 경영 효율성을 증대시킴으로써 공공기관의 재무적 안정성을 확보하고자 했던 정부의 개혁 전략이 유효하게 작용할 수 있음을 시사한다.

• Communications for Statistical Applications and Methods
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• 제4권1호
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• pp.101-109
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• 1997

For the detection on variance changes in the nonstationary time series with a unit root two types of test statistics are proposed, of which one is based on the cumulative sum of squares and the other is based on the likelihood ratio test. The properties of the cusum type test statistic are derived and the performance of two tests in small samples are compared through Monte Carlo study. It is ovserved that the test based on the cumulative sum of squares can detect a samll change in the variance faster than the one based on the likelihood ratio.

• Safety and Health at Work
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• 제3권4호
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• pp.294-297
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• 2012

Previous studies on the vast increase in suicide mortality in Southeast Asia have indicated that suicide rates increase in parallel with a rise in unemployment or during periods of economic recession. This paper examines the effects of economic recession on suicidal rates amongst agriculture, fisheries, and forestry workers in Korea. Monthly time-series gross domestic product (GDP) data were linked with suicidal rates gathered from the cause of death records between1993-2008. Data were analyzed using generalized additive models to analyze trends, while a polynomial lag model was used to assess the unconstrained time lag effects of changes in GDP on suicidal rate. We found that there were significant inverse correlations between changes in GDP and suicide for a time lag of one to four months after the occurrence of economic event. Furthermore, it was evident that the overall relative risks of suicide were high enough to bring about social concern.